Exploring a Cost-Effective Approach to AGV Solutions: A Case Study in the Textile Industry

Current trend for Factory Automation is to design a variety of products to meet the changing demands of the customers. Automated material handling is an integral part of any automated manufacturing system. Automated Guided Vehicle (AGVs) is emerging as intelligent Material Handling Equipments (MHE) that is capable of adjusting to the changing scenario of manufacturing systems. AGVs are battery-powered, automated vehicles with capabilities to tag along programmed motions and orientation. AGVs selection has always been a challenging task owing to several constraints of manufacturing systems. Different MCDM (Multiple Criteria Decision Making) approaches have been employed by researchers for selection of MHE problem. This work is an attempt to choose amongst the AGVs alternatives and analyze their performance using certain MCDM approaches such as AHP, DEMATEL, TOPSIS, Fuzzy AHP, Fuzzy DEMATEL and Fuzzy TOPSIS. In the present work, data has been taken from Maniya and Bhatt [27] and the problem has been evaluated using four integrated approaches for analysis of AGV’s selection problem. It was found that Fuzzy AHP-Fuzzy TOPSIS and Fuzzy DEMATEL—Fuzzy TOPSIS technique gave better results for the instant case as they were devoid of vagueness error caused due to uncertainty in the decision making of experts. Authors have applied four approaches out of which DEMATEL-TOPSIS and Fuzzy DEMATEL-Fuzzy TOPSIS approaches has yet not been applied in AGVs selection problem, thus justifying original work and their appropriateness for the problem.

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.

The application of Automated Guided Vehicles (AGVs) for workpiece transportation in production processes is commonplace, particularly in the context of Industry 4.0. However, the high cost associated with AGVs presents a significant challenge. This paper proposes an approach to AGV tracking using a combination of camera-based QR code detection and ultrasonic sensors. The objective is to regulate the distance between leading and following AGVs, thereby addressing the operational costs associated with expensive equipment. Experimental investigations were conducted to assess the performance of QR symbols and color bars under varying lighting conditions, ranging from 200 lux to 650 lux. Accurate distance measurements were achieved within a range of 150 cm. In lower lighting conditions (100 lux to 250 lux), the RGB color bar detection failed at distances below 40 cm, whereas QR Code detection remained robust. Angle measurements demonstrated accurate readings up to 30 degrees from the camera’s center, within distances of 50 to 125 cm, with an anticipated deviation of no more than 0.5 degrees. Subsequently, QR code symbols were chosen as the experimental control symbol, complemented by ultrasonic sensors for inter-vehicle distance maintenance. During practical testing, if the measured distance falls below 60 cm. while moving in a straight line, the motor ceases operation. Conversely, if the distance exceeds 80 cm, the motor activates, allowing the follower AGV to uphold the specified distance behind the leader AGV.

PurposeThis paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system.Design/methodology/approachConsidering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production.FindingsThe model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example.Practical implicationsA case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized.Originality/valueThe benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

This paper considers the problem of real-time dispatching of a fleet of automated guided vehicles (AGVs) with battery constraints. AGVs must be immediately assigned to transport requests, which arrive randomly. In addition, the AGVs must be repositioned and recharged, awaiting future transport requests. Each transport request has a soft time window with late delivery incurring a tardiness cost. This research aims to minimize the total costs, consisting of tardiness costs of transport requests and travel costs of AGVs. We extend the existing literature by making a distinction between parking and charging nodes, where AGVs wait idle for incoming transporting requests and satisfy their charging needs, respectively. Also, we formulate this online decision-making problem as a Markov decision process and propose a solution approach based on deep reinforcement learning. To assess the quality of the proposed approach, we compare it with the optimal solution of a mixed-integer linear programming model that assumes full knowledge of transport requests in hindsight and hence serves as a lower-bound on the costs. We also compare our solution with a heuristic policy used in practice. We assess the performance of the proposed solutions in an industry case study using real-world data.

Future smart manufacturing systems will include more complex coordination of mobile manipulators (i.e., robot arms mounted on mobile bases). The National Institute of Standards and Technology (NIST) conducts research on the safety and performance of multiple collaborating robots using a mobile platform, an automatic guided vehicle (AGV) with an onboard manipulator. Safety standards for robots and industrial vehicles each mandate their failsafe control, but there is little overlap between the standards that can be relied on when the two systems are combined and their independent controllers make collaborative decisions for safe movement. This paper briefly discusses previously uncovered gaps between AGV and manipulator standards and details decision sharing for when manipulators and AGVs are combined into a collaborative, mobile manipulator system. Tests using the NIST mobile manipulator with various control methods were performed and are described along with test results and plans for further, more complex tests of implicit and explicit coordination control of the mobile manipulator.

Trajectory planning for AGVs in automated container terminals using avoidance constraints: a case study

As a kind of intelligent car, automated guided vehicle (AGV) belongs to the application direction of wheeled robots and has been widely used in various industrial fields. Because of complex and unpredictable working environment, the obstacle avoidance of AGV has attracted much attention. In this paper, multi-sensor information fusion technology based on back propagation (BP) fuzzy neural network is applied to AGV vehicle obstacle avoidance. Five combinations of infrared sensor and ultrasonic sensor are used to detect the distance of obstacles and one laser sensor is used to scan the direction of targets. The collected data of multiple sensors are fused twice. The simulation results of MATLAB demonstrate the effectiveness of the proposed method.

Automated guided vehicles (AGV) are future products that can provide driverless transportation and can transport a variety of products without manual intervention in production, logistics, warehouse and distribution environments. The main advantage of AGV is that they can operate as independent systems with higher efficiency. The proposed work has the main function of AGV, of which EPROM is used to save data. Taking advantage of EPROM, the robot can maintain and continue to execute the current program even if the system returns to the power-on state after a period of power failure in a crisis situation. Compared with manual functions, AGV provides higher efficiency for many industries. The ultrasonic sensor assists the controller, which can realize the obstacle detection function in the robot. Through the artificial intelligence controlled by Bluetooth/Wi-Fi, AGV can be further improved to the level of automation.

To address the challenges of path conflicts and communication in cooperative operation of dual Automated Guided Vehicles (AGVs) in industrial logistics, a low-cost intelligent dual-AGV calling system based on STM32 microcontroller and ZigBee wireless communication technology is proposed. The system employs the STM32F103C8T6 as the core controller, integrating TB6612 motor driver, TCRT5000 infrared tracking, and HC-SR04 ultrasonic ranging modules to achieve autonomous navigation and dynamic obstacle avoidance for AGVs. A star communication network is established via ZigBee for transmitting instructions from the host computer and receiving AGV status information. The core innovation lies in a dynamic avoidance strategy based on random priority decision-making: when the ultrasonic sensors detect an inter-AGV distance less than 40cm, a random number generation mechanism (seeded by ultrasonic ranging data) is triggered. Decision information is exchanged via ZigBee, and the AGV with the higher number gains priority passage. Experiments demonstrate that the system stably realizes dual-AGV cooperative calling, path tracking, and intersection conflict resolution, with an average response time of approximately 185ms. ZigBee communication exhibits a delay below 50ms and a packet loss rate less than 0.5%, providing a highly reliable and low-cost solution for flexible production line logistics.

Industry 4.0: Sustainable material handling processes in industrial environments

Technological advances support the development of tools used in the industrial sector, one of which is the Automated Guided Vehicle (AGV). AGV can be used to deliver goods automatically. This AGV can be made using visual sensors, one of which is the Pixy camera. This AGV robot can be used to follow the motion of objects. In addition to the Pixy camera as a visual sensor, the Arduino Mega 2560 is also used as a microcontroller, and the L298N motor driver shield with two DC motors. The distance and speed of the robot are also determined so that it can move in the direction the object is going. Manoeuvring is needed on a robot with better sharpness and ability, so Mamdani fuzzy logic control is used as a method. Tests are carried out using the Turn value or Position and Area or the distance between the camera sensor and the object. The experimental simulation was carried out with a case study of Turn value of 0.6 and Area of 6000. Testing was carried out using simulation software and Arduino IDE which were then calculated manually for comparison. The results obtained in testing with the simulation software are 20.80 for the right DC motor and 85.60 for the left DC motor, with the Arduino IDE software the values are 21.11 for the right DC motor and 85.20 for the left DC motor, and the manual calculation produces a value of 21.09 for the right DC motor and 85.04 for the left DC motor. From the resulting data, from the error rate is 98.51% for the right DC motor and 99.53% for the left DC motor.

Cross-docking is important for logistics because it reduces inventory, lead-times, and shipments. However, dynamic imbalances between supply and demand usually results in some inventory being warehoused in cross-docks. Therefore, flexible automation using robots, such as automated guided vehicles (AGV), can be used to improve performance of cross-docking, a trend in industry. This paper focuses on mathematical modeling of cross-docking operations when packages are moved in AGVs. A model based on Mean Value Analysis (MVA) is developed for determining the number of AGVs and estimating the service rate. This service rate is used as a parameter in a fork/join queuing model to characterize the performance of cross-docks in which outbound trucks get their packages directly from in-bound trucks as well as warehouses by estimating the queue lengths and mean sojourn times. The efficacy of the combined MVA and fork/join analytical models is verified using a discrete-event simulation as a case study, which shows that the models are largely in agreement in a test range with difference of 28% at the lowest throughput tested. Future study could use advances in sensors and Industry 4.0 for estimating parameters in the proposed models and improve performance and sustainability.

This demonstration presents Flexonomy, a flexible autonomous system for distributed coordination, transportation and localisation in a lab-scale factory floor. It illustrates the use of multi-agent systems in manufacturing and leverages new Industry 4.0 design principles to cope with manufacturing requirements in factories of the future: rapidly changing customer needs, market volatility and shortened product life cycles. Three main contributions are identified: (i) distributed auction-based coordination allows local decision making and task allocation, (ii) distributed model predictive control-based transportation enables free space collision avoidance of automated guided vehicles (AGVs), and (iii) distributed vision-based localisation provides scalable and dynamic position information of key resources on the factory floor.

An automated warehouse system contains a number of materials, workstations, and multiple Automated Guided Vehicles (AGVs). The automated warehouse is server-controlled. This paper proposes a collision-free routing method for AGVs based on collision classification. This method can deal with collisions arising in the automated warehouse. It first divides the warehouse environment into five areas, and then performs route planning. In this paper, the environment map for AGVs is described by using the grid method. The initial route of each task is predetermined by improved Dijkstra’s algorithm. The server detects the potential collisions by comparing each workstation’s ID and corresponding time window in every route. This paper presents four collision classifications and three solutions. Based upon the analyses and experiments, we select the corresponding solution for each type of collision. Presented case studies demonstrate the efficiency of the proposed collision-free route planning approach.